personalized2part: Two-Part Estimation of Treatment Rules for Semi-Continuous Data

Implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.

Version: 0.0.1
Depends: personalized, HDtweedie
Imports: Rcpp, foreach, methods
LinkingTo: Rcpp, RcppEigen
Published: 2020-09-10
DOI: 10.32614/CRAN.package.personalized2part
Author: Jared Huling ORCID iD [aut, cre]
Maintainer: Jared Huling <jaredhuling at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: personalized2part citation info
Materials: README
CRAN checks: personalized2part results


Reference manual: personalized2part.pdf


Package source: personalized2part_0.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): personalized2part_0.0.1.tgz, r-oldrel (arm64): personalized2part_0.0.1.tgz, r-release (x86_64): personalized2part_0.0.1.tgz, r-oldrel (x86_64): personalized2part_0.0.1.tgz


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